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What Does 'Composite AI' Mean, and How Are Tech Leaders Using It?


According to Gartner, Composite AI refers to “the combined application (or fusion) of different AI techniques to improve the efficiency of learning to broaden the level of knowledge representations.” Garter explains that Composite AI provides a platform to solve a broader range of business problems more effectively through what are known as “AI abstraction mechanisms.”

Another way to frame it – perhaps one that’s a bit simpler – is this: Composite AI, as an umbrella-esque term, combines different (and often abstract) approaches to harnessing artificial intelligence to achieve better business results. In this case, “different approaches” include machine learning (ML), generative AI (GenAI), predictive utilizations of AI, natural language processing (NLP), data mining, computer vision, and many others. It basically depends on what business problem requires solving. (Another helpful breakdown of Composite AI, courtesy of SAS, can be read here.)

Recently, Dynatrace – with teams that combine deep observability, AIOps, and application security in a unified and precise intelligent data platform – released the official findings of an independent survey of approximately 1,300 CTOs and CIOs (amongst other technology leaders). This research detailed organizations’ increased investments in artificial intelligence; investments aimed at boosting productivity, automating routine tasks, reducing operational costs, and keeping pace with 1.) the competition of today, and 2.) the deep proliferation of AI, in general.

Composite AI, in this vein, was covered quite a bit and was juxtaposed with both the benefits and challenges that must be managed and overcome in order to truly support business-critical use cases in 2024 and beyond.

Here are some of the long-story-short findings and other datapoints that Dynatrace pulled together.

From the surveyed technology leaders and their organizations:

  • 3% of technology leaders are concerned that AI could be used for non-approved uses as employees become more accustomed to using tools such as ChatGPT.
  • 95% are concerned that using GenAI to create code could result in leakage and improper or illegal use of intellectual property.
  • 98% concerned that GenAI could be susceptible to unintentional bias, error, and misinformation.
  • 95% of technology leaders say GenAI would be more beneficial if enriched and prompted by other types of AI that can provide precise facts about current states and accurate predictions about the future.
  • 83% say AI has become mandatory to keep up with the dynamic nature of cloud environments.
  • 82% say AI will be critical to security threat detection, investigation, and response.
  • 88% expect AI to extend access to data analytics to nontechnical employees through natural language queries.
  • 62% have already changed the job roles and skills they are recruiting for because of AI.
  • 88% think AI will enable cloud cost efficiencies by supporting FinOps practices.
  • 61% will increase investment in AI over the next 12 months to speed up development by automatically generating code.

According to Dynatrace’s own CTO, Bernd Greifeneder:

“AI has become central to how organizations drive efficiency, improve productivity, and accelerate innovation. The release of ChatGPT late last year triggered a significant GenAI hype cycle. Business, development, operations, and security leaders have set high expectations for GenAI to help them deliver new services with less effort and at record speeds. However, as organizations endeavor to realize the expected value, it becomes evident that GenAI requires domain-specific tuning and integration with other technologies, including other types of AI. In addition, organizations must use AI securely and responsibly and monitor it closely to manage cost and user experience. This will help them provide accurate results, reduce expenses, and prevent employees from exposing sensitive data or creating vulnerabilities in their environments.”

Edited by Greg Tavarez
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